Neural Networks - Back Propogation
1
$begingroup$
The following code is my implementation of neural network (1 hidden layer) trying to predict some number based on input data.
- Number of input node: 11
- Number of nodes in hidden layer: 11
- Number of nodes in output layer: 1
m: number of training examples, here = 4527
X:[11, m]matrix
y:[1, m]matrix
w1: weights associated from input layer to hidden layer
b1: bias vector associated from input layer to hidden layer
w2: weights associated from hidden layer to output layer
b2: bias vector associated from hidden layer to output layer
alpha: learning rate
ite: number of iteration, here = 10000
Since I'm trying to predict a continuous value output, I'm using sigmoid function in input layers and identity function in output layer
def propagate(X,y,w1,b1,w2,b2,alpha,ite):
assert(X.shape[0] == 11)
assert(y.shape[0] == 1)
assert(X.shape[1] == y.shape[1])
m = X.shape[1]
J = np.zeros(shape=(ite,1))
iteNo = np.zeros(shape=(ite,1))
for i in range(1,ite+1):
z1 = np.dot(w1,X) + b1
a1 = sigmoid(z1)
z2 = np.dot(w2,a1) + b2
dz2 = (z2-y)/m
dw2 = np.dot(dz2,a1.T)
db2 = np.sum(dz2, axis=1, keepdims=True)
dz1 = np.dot(w2.T,dz2)*derivative_of_sigmoid(z1)
dw1 = np.dot(dz1,X.T)
db1 = np.sum(dz1, axis=1, keepdims=True)
w2 = w2 - (alpha*dw2)
b2 = b2 - (alpha*db2)
w1 = w1 - (alpha*dw1)
b1 = b1 - (alpha*db1)
iteNo[i-1] = i
J[i-1] = np.dot((z2-y),(z2-y).T)/(2*m)
print(z2)
return w1,b1,w2,b2,iteNo,J
I have tried both the ways (With feature normalization and scaling & without) but my cost function varies as follows with respect number of iterations (Plotted J).
On $x$-axis: Number of iteration, On $y$-axis: Error $times 10^{12}$.
Please help!
machine-learning python neural-network
edited yesterday
Siong Thye Goh
1,122418
asked yesterday
ChiragChirag
61
New contributor
Chirag is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
1
$begingroup$
The following code is my implementation of neural network (1 hidden layer) trying to predict some number based on input data.
- Number of input node: 11
- Number of nodes in hidden layer: 11
- Number of nodes in output layer: 1
m: number of training examples, here = 4527
X:[11, m]matrix
y:[1, m]matrix
w1: weights associated from input layer to hidden layer
b1: bias vector associated from input layer to hidden layer
w2: weights associated from hidden layer to output layer
b2: bias vector associated from hidden layer to output layer
alpha: learning rate
ite: number of iteration, here = 10000
Since I'm trying to predict a continuous value output, I'm using sigmoid function in input layers and identity function in output layer
def propagate(X,y,w1,b1,w2,b2,alpha,ite):
assert(X.shape[0] == 11)
assert(y.shape[0] == 1)
assert(X.shape[1] == y.shape[1])
m = X.shape[1]
J = np.zeros(shape=(ite,1))
iteNo = np.zeros(shape=(ite,1))
for i in range(1,ite+1):
z1 = np.dot(w1,X) + b1
a1 = sigmoid(z1)
z2 = np.dot(w2,a1) + b2
dz2 = (z2-y)/m
dw2 = np.dot(dz2,a1.T)
db2 = np.sum(dz2, axis=1, keepdims=True)
dz1 = np.dot(w2.T,dz2)*derivative_of_sigmoid(z1)
dw1 = np.dot(dz1,X.T)
db1 = np.sum(dz1, axis=1, keepdims=True)
w2 = w2 - (alpha*dw2)
b2 = b2 - (alpha*db2)
w1 = w1 - (alpha*dw1)
b1 = b1 - (alpha*db1)
iteNo[i-1] = i
J[i-1] = np.dot((z2-y),(z2-y).T)/(2*m)
print(z2)
return w1,b1,w2,b2,iteNo,J
I have tried both the ways (With feature normalization and scaling & without) but my cost function varies as follows with respect number of iterations (Plotted J).
On $x$-axis: Number of iteration, On $y$-axis: Error $times 10^{12}$.
Please help!
machine-learning python neural-network
edited yesterday
Siong Thye Goh
1,122418
asked yesterday
ChiragChirag
61
New contributor
Chirag is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
1
1
1
$begingroup$
The following code is my implementation of neural network (1 hidden layer) trying to predict some number based on input data.
- Number of input node: 11
- Number of nodes in hidden layer: 11
- Number of nodes in output layer: 1
m: number of training examples, here = 4527
X:[11, m]matrix
y:[1, m]matrix
w1: weights associated from input layer to hidden layer
b1: bias vector associated from input layer to hidden layer
w2: weights associated from hidden layer to output layer
b2: bias vector associated from hidden layer to output layer
alpha: learning rate
ite: number of iteration, here = 10000
Since I'm trying to predict a continuous value output, I'm using sigmoid function in input layers and identity function in output layer
def propagate(X,y,w1,b1,w2,b2,alpha,ite):
assert(X.shape[0] == 11)
assert(y.shape[0] == 1)
assert(X.shape[1] == y.shape[1])
m = X.shape[1]
J = np.zeros(shape=(ite,1))
iteNo = np.zeros(shape=(ite,1))
for i in range(1,ite+1):
z1 = np.dot(w1,X) + b1
a1 = sigmoid(z1)
z2 = np.dot(w2,a1) + b2
dz2 = (z2-y)/m
dw2 = np.dot(dz2,a1.T)
db2 = np.sum(dz2, axis=1, keepdims=True)
dz1 = np.dot(w2.T,dz2)*derivative_of_sigmoid(z1)
dw1 = np.dot(dz1,X.T)
db1 = np.sum(dz1, axis=1, keepdims=True)
w2 = w2 - (alpha*dw2)
b2 = b2 - (alpha*db2)
w1 = w1 - (alpha*dw1)
b1 = b1 - (alpha*db1)
iteNo[i-1] = i
J[i-1] = np.dot((z2-y),(z2-y).T)/(2*m)
print(z2)
return w1,b1,w2,b2,iteNo,J
I have tried both the ways (With feature normalization and scaling & without) but my cost function varies as follows with respect number of iterations (Plotted J).
On $x$-axis: Number of iteration, On $y$-axis: Error $times 10^{12}$.
Please help!
machine-learning python neural-network
edited yesterday
Siong Thye Goh
1,122418
asked yesterday
ChiragChirag
61
New contributor
Chirag is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
The following code is my implementation of neural network (1 hidden layer) trying to predict some number based on input data.
- Number of input node: 11
- Number of nodes in hidden layer: 11
- Number of nodes in output layer: 1
m: number of training examples, here = 4527
X:[11, m]matrix
y:[1, m]matrix
w1: weights associated from input layer to hidden layer
b1: bias vector associated from input layer to hidden layer
w2: weights associated from hidden layer to output layer
b2: bias vector associated from hidden layer to output layer
alpha: learning rate
ite: number of iteration, here = 10000
Since I'm trying to predict a continuous value output, I'm using sigmoid function in input layers and identity function in output layer
def propagate(X,y,w1,b1,w2,b2,alpha,ite):
assert(X.shape[0] == 11)
assert(y.shape[0] == 1)
assert(X.shape[1] == y.shape[1])
m = X.shape[1]
J = np.zeros(shape=(ite,1))
iteNo = np.zeros(shape=(ite,1))
for i in range(1,ite+1):
z1 = np.dot(w1,X) + b1
a1 = sigmoid(z1)
z2 = np.dot(w2,a1) + b2
dz2 = (z2-y)/m
dw2 = np.dot(dz2,a1.T)
db2 = np.sum(dz2, axis=1, keepdims=True)
dz1 = np.dot(w2.T,dz2)*derivative_of_sigmoid(z1)
dw1 = np.dot(dz1,X.T)
db1 = np.sum(dz1, axis=1, keepdims=True)
w2 = w2 - (alpha*dw2)
b2 = b2 - (alpha*db2)
w1 = w1 - (alpha*dw1)
b1 = b1 - (alpha*db1)
iteNo[i-1] = i
J[i-1] = np.dot((z2-y),(z2-y).T)/(2*m)
print(z2)
return w1,b1,w2,b2,iteNo,J
I have tried both the ways (With feature normalization and scaling & without) but my cost function varies as follows with respect number of iterations (Plotted J).
On $x$-axis: Number of iteration, On $y$-axis: Error $times 10^{12}$.
Please help!
machine-learning python neural-network
machine-learning python neural-network
edited yesterday
Siong Thye Goh
1,122418
asked yesterday
ChiragChirag
61
New contributor
Chirag is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited yesterday
Siong Thye Goh
1,122418
asked yesterday
ChiragChirag
61
New contributor
Chirag is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited yesterday
Siong Thye Goh
1,122418
edited yesterday
Siong Thye Goh
1,122418
edited yesterday
Siong Thye Goh
1,122418
1,122418
asked yesterday
ChiragChirag
61
New contributor
Chirag is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
ChiragChirag
61
asked yesterday
ChiragChirag
61
61
New contributor
Chirag is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Chirag is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Chirag is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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